基于Rhapsody的综合数据管理软件设计与实现

介绍了基于Rhapsody的面向对象的设计方法在综合数据管理软件中的应用。对软件的功能进行了描述,阐述了采用面向对象的方法所设计的类及其操作。对其中需要起动任务运行的类的状态图进行描绘,对软件中各元素的集成也进行了说明。采用该方法设计并实现的软件已经在某飞机上运行并得到了验证,其功能和性能能够满足飞机的使用需求。     

Real-time modular dynamic modeling for compression system of altitude ground test facilities

Modeling of a centrifugal compressor is of great significance to surge characteristics and fluid dynamics in the Altitude Ground Test Facilities (AGTF). Real-time Modular Dynamic System Greitzer (MDSG) modeling for dynamic response and simulation of the compression system is introduced. The centrifugal compressor, pipeline network, and valve are divided into pressure output type and mass flow output type for module modeling, and the two types of components alternate when the system is established. The pressure loss and thermodynamics of the system are considered. An air supply compression system of AGTF is modeled and simulated by the MDSG model. The simulation results of mass flow, pressure, and temperature are compared with the experimental results, and the error is less than 5%, which demonstrates the reliability, practicability, and universality of the MDSG model.     

基于ViBE与面向对象分类的视频卫星运动车辆检测

为了提高视频卫星对运动车辆的检测质量，在经典视觉背景提取器(ViBE)算法的基础上，结合遥感的面向对象分类技术，从提升正确检测运动目标数量和抑制虚假运动目标检测数量两个方面着手，提出了一种新的运动车辆检测方法（VOMVD）。首先通过优化ViBE模型参数，尽可能多地获取真实运动目标，但这在一定程度引入了许多的虚假目标。研究继而依据影像上地面小尺度运动目标和道路的依存关系，采用面向对象的分类方法，基于光谱、纹理、空间属性，构建了均值、标准差、卷积核内平均灰度值、卷积核内平均信息熵、面积、长度、紧密度、延伸度等8个特征，用于提取道路信息，以此掩膜ViBE提取的虚假运动目标和伪运动目标。结果表明，基于本研究提出的视频卫星运动目标检测方法较之三帧差分法、ViBE检测方法等，其精度有明显提升。在本研究中，三帧差分法、ViBE和VOMVD对运动目标的检测精度P分别为70.91%，61.49%和85.71%，召回率R分别为84.78%，98.91%和97.83%，F值分别为77.23%，75.83%和91.37%，有效提升了方法对运动目标的检测效果。     

空间大质量目标转位操作双臂协调控制方法

针对空间中双臂协调转位过程中面临的双臂异构、刚度不同、负载较大、测量/执行误差影响大、抓捕位置随机偏差等问题，提出了一种空间大质量目标转位操作双臂协调控制方法。首先基于双臂转位的几何约束，进行协调转位运动规划。然后，将双机械臂关节划分为主动关节和从动关节，并对主动关节设计关节位置控制器，对从动关节设计零力控制器，从动关节在转位目标的拖动力下进行跟随运动。最后，数学仿真表明了所提出的双臂协调转位方法的有效性。     

Rational design of self-powered sensors with polymer nanocomposites for human–machine interaction

Smart sensors are becoming one of the necessities for connecting and detecting surrounding stimuli with tremendous convenience, especially when exploiting a single powerful sensor with multifunctionality. To successfully accomplish the design of a self-powered sensor, serving power is becoming a critical issue because of its continuously consumed energy required by electronics. A variety of nanogenerators aiming for the rational design of self-powered system are reviewed and compared, followed by their recent advances with polymer nanocomposites for self-powered sensors. More importantly, the proposed conceptual design of a self-powered unit/device with triboelectric nanogenerator has been emphasized to eventually realize the practical activities towards multiple detections and human–machine interaction. Finally, challenges and new prospects of rational design of self-powered polymer composite sensors in achieving human–machine interaction/interface are discussed.     

基于数据扩充和特征增强的雷达回波无人机检测

针对基于雷达无人机目标识别难度高、精确度低、适应性差等问题，本文提出了利用深度学习的方法，对雷达回波进行无人机检测。首先，利用相参累积的方法生成雷达回波的距离-多普勒图像，增强目标特征并提高信噪比;其次，采用生成对抗模型对距离-多普勒图像进行数据扩充，以获得充足的图像数据减小网络的过拟合并提高网络鲁棒性;最后，使用基于位置感知的卷积神经网络增强特征，通过构建基于距离-多普勒图像的感知模块，实现对目标距离和运动速度的检测。通过在雷达回波序列中弱小飞机目标检测跟踪数据集上验证的结果表明:最终检测结果在召回率89%的情况下达到了91%的准确率。相比于基准方法，本文提出的方案具有更高的检测精度和更好的网络运行效率。     

An air-rail inter-modal strategy for aircraft recovery

It is common that airlines encounter a disruption of a flight schedule, which is mainly caused by resource shortages. In case of a disruption, subject to scarce resources, most of airlines lose flexibility of performing aircraft recovery on the basis of business interest priorities and need to delay, swap, and cancel flights. This paper proposes an air-rail inter-modal strategy to incorporate a High-Speed Rail(HSR) transport mode into an aviation network for aircraft recovery. The air-rail inter...     

基于空间分布特性多层RX高光谱点目标检测

针对目前高光谱图像异常点目标检测过程中准确率低和虚警率高的问题,本文结合目标的空间分布特性,提出了一种多层级RX检测方法。通过计算被检测区域图像谱向相似性响应图,采用非线性抑制的方法,突出点目标并抑制背景。为进一步提高检测算法的表达能力和泛化性能,采用多级检测器级联的方式,逐层级增强异常点处的相对能量,削弱背景的影响,从而达到较高的检测性能。在外场挂飞试验数据集上进行验证,结果表明:该方法 AUC值达到0.988 1,明显优于CEM算法的0.962 6和传统RX算法的0.939 2。     

Modelling and analysis of initial icing roughness with fixed-grid enthalpy method based on DPM-VOF algorithm

Ice particles could form under the continuous impingement of incoming supercooled droplets in icing conditions, which will change the surface roughness to enhance the further heat and mass transfer during icing process. A fixed-grid porous enthalpy method based on the improved Discrete Phase Model(DPM) and Volume of Fluid(VOF) integrated algorithm is developed to solve the multiphase heat transfer problem to give more detailed demonstration of the formation of initial ice roughness. The algorith...     

顾及动态物体感知的增强型视觉SLAM系统

传统的同步定位与制图（Simultaneous localization and mapping, SLAM）系统在复杂环境下工作时，无法分辨环境中的物体是否存在运动状态，图像中运动的物体可能导致特征关联错误，引起定位的不准确和地图构建的偏差。为了提高SLAM系统在动态环境下的鲁棒性和可靠性，本文提出了一种顾及动态物体感知的增强型视觉SLAM系统。首先，使用深度学习网络对每一帧图像的动态物体进行初始检测，然后使用多视图几何方法更加精细地判断目标检测无法确定的动态物体区域。通过剔除属于动态物体上的特征跟踪点，提高系统的鲁棒性。本文方法在公共数据集TUM和KITTI上进行了测试，结果表明在动态场景中定位结果的准确度有了明显提升，尤其在高动态序列中相对于原始算法的精度提升在92%以上。与其他顾及动态场景的SLAM系统相比，本文方法在保持精度优势的同时，提高了运行结果的稳定性和时间效率。